Microsoft AutoGen Connector for AI agents.
10 live capabilities
Coordinate multi-agent workflows and manage complex agentic swarms.
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Why people use Microsoft AutoGen
AutoGen Multi-Agent Workflows for Complex Software Engineering
With this Connector, you can command a whole swarm of specialized agents through a natural conversation. You get a clear window into the internal mechanics of your multi-agent system, making it easy to debug logic and manage roles without the manual overhead.
What Vinkius changes
That you get a remote control for your multi-agent swarm.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Debugging a stuck coder
An AI engineer sees a coder agent looping.
- Real-world use case 02
Launching a market research swarm
A researcher asks the agent to start a research workflow, which creates a session and routes messages to a swarm.
- Real-world use case 03
Auditing production agents
A PM uses list_agents and list_models to verify that the production swarm is using the correct roles and models.
Complete set · 10capabilities
The complete Microsoft AutoGen capability set.
These are the exact actions your AI can choose when you ask it to work with Microsoft AutoGen.
01—04
4 capabilities in this set.
Part of 10 available through Microsoft AutoGen.
- 01 Capability
Create message
Send a human prompt to start or continue a conversation. It acts as the primary way to trigger actions from your AI client.
- 02 Capability
Create agent
Define a new customized agent with specific roles and parameters. This helps you build a team of specialists instead of one generalist.
- 03 Capability
Delete session
Permanently remove a session to clear out old memory and data. Use this to wipe failed experiments quickly.
- 04 Capability
List agents
View all the agents currently configured in your AutoGen instance. This gives you a quick overview of your entire agentic team.
05—07
3 capabilities in this set.
Part of 10 available through Microsoft AutoGen.
- 05 Capability
List messages
Pull the full history of messages for a specific session. It is essential for debugging how agents are interacting behind the scenes.
- 06 Capability
List models
Check which LLMs are currently active and configured in your engine. Use this to audit your model constraints and fallbacks.
- 07 Capability
List sessions
See a list of all your active and past conversation sessions. This helps you track multiple ongoing workflows at once.
08—10
3 capabilities in this set.
Part of 10 available through Microsoft AutoGen.
- 08 Capability
List skills
See all the Python functions your agents can use to perform actions. This lets you know exactly what code your swarm can execute.
- 09 Capability
List workflows
Browse the predefined multi-agent workflows available in your studio. This helps you understand the routing topographies of your system.
- 10 Capability
Create session
Open a fresh, isolated memory space for a new multi-agent workflow. This keeps different projects from bleeding into each other.
Set up in minutes
One URL. Then ask Microsoft AutoGen to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Microsoft AutoGen from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Microsoft AutoGen, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Microsoft AutoGen for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Microsoft AutoGen URL.
- Step 03
Save and start
Save the connection and enable Microsoft AutoGen in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"autogen": {
"url": "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Microsoft AutoGen
Open Agent mode in chat and ask: "Using Microsoft AutoGen, help me...". 10 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"autogen": {
"url": "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Microsoft AutoGen
Ask Copilot: "Using Microsoft AutoGen, help me...". 10 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"autogen": {
"url": "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Microsoft AutoGen
Open Cascade and ask: "Using Microsoft AutoGen, help me...". 10 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"autogen": {
"url": "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Microsoft AutoGen
Ask Cline: "Using Microsoft AutoGen, help me...". 10 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add autogen --transport http "https://edge.vinkius.com/vk_preview_im6ZUleGV5zCo8b51ydydKQxlYTOsNTFUZKM0ez4/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Microsoft AutoGen
Ask Claude: "Using Microsoft AutoGen, show me...". 10 tools are ready
Where the request belongs
Work Microsoft AutoGen can move forward.
For AI engineers and researchers who are tired of single-agent hallucinations and need a structured way to coordinate multiple specialized models for complex production tasks.
AI Engineer
Debugging agentic loops and tracing multi-agent logs to find where logic fails.
Product Manager
Verifying the health of automated backend swarms without reading raw code.
Research Scientist
Extracting conversational histories across different experimental boundaries for grading.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsLangflow (Visual Multi-agent Orchestrator)
Orchestrate multi-agent AI workflows visually. execute flows, manage projects, and trigger webhooks directly from any AI agent.
Dify
Manage agentic workflows via Dify. send chat messages, track conversations, audit app parameters, and handle file uploads directly from any AI agent.
Lindy (Autonomous AI Employees)
Manage autonomous AI employees via Lindy. trigger task runs, monitor reasoning logs, and audit app integrations.
LangGraph Cloud (Stateful AI Agents)
Orchestrate stateful AI agents via LangGraph Cloud. manage assistants, monitor conversation threads, and handle human-in-the-loop overrides.
AirOps
AI workflow orchestration. execute models, manage agents, and query memory via AI.
CrewAI Platform
Orchestrate multi-agent workflows via CrewAI. list crews and agents, kickoff autonomous runs, and monitor task execution directly from any AI agent.
Bring your own AI
Change the model, client or framework. Keep Microsoft AutoGen connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Microsoft AutoGen.
The practical details behind the request, access and result.
Can I use AutoGen MCP to manage a team of AI agents?
Yes, this Connector lets you orchestrate multi-agent swarms. You can define roles, manage how they talk to each other, and monitor their progress from your primary AI client.
How does AutoGen MCP help with multi-agent workflows?
It provides a direct bridge to your AutoGen Studio. You can launch workflows, view routing topographies, and see how different agents interact to complete a goal.
Can I see the history of what my agents are saying to each other?
Yes, you can retrieve deep conversation traces. This is perfect for debugging complex loops where you need to see the back-and-forth between a coder, a critic, or a proxy.
Does AutoGen MCP work with my existing AutoGen Studio setup?
It does. You just need to provide your AutoGen Studio Base URL. It connects your AI client directly to your existing instance so you can manage it naturally.
How do I keep my different AI projects from getting mixed up?
The Connector allows you to create isolated sessions. Each session has its own memory space, so your research data won't bleed into your coding or production tasks.
Can I see what Python capabilities my agents can actually use?
Yes, you can list all the Python skills available in your studio. This gives you a clear view of what code your agents can execute on your behalf.
Can my AI agent debug a looping multi-agent conversation?
Yes. You can instruct your primary agent to retrieve the message traces for a specific AutoGen session ID. It will instantly unpack the internal LLM-to-LLM conversation, highlighting exactly which secondary agent is looping, throwing errors, or deviating from the constraints without manual log parsing.
How do I add a new Python capability or skill dynamicly?
Your agent can list currently mapped Python skills bound to the studio runtime. If you need a new capability, your primary AI can iterate on the script directly on your CLI/editor and once deployed in your studio, you can map it natively to customized agents via the creation parameters.
Can it trigger a Workflow to start executing a new complex task?
Absolutely. Ask your agent to create a fresh, blank, and completely isolated session, then dispatch a newly constructed 'human message' targeting an existing Multi-Agent workflow topology. It initiates the whole automated logic sequence securely and remotely.
One connection away
Give your agent a direct line to Microsoft AutoGen.
Connect Microsoft AutoGen once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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